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---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_1x_deit_base_adamax_00001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7073170731707317
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hushem_1x_deit_base_adamax_00001_fold5
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co./facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6418
- Accuracy: 0.7073
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 6 | 1.3016 | 0.3415 |
| 1.352 | 2.0 | 12 | 1.2693 | 0.4878 |
| 1.352 | 3.0 | 18 | 1.2337 | 0.4878 |
| 1.192 | 4.0 | 24 | 1.1939 | 0.5122 |
| 1.066 | 5.0 | 30 | 1.1544 | 0.5854 |
| 1.066 | 6.0 | 36 | 1.1118 | 0.5854 |
| 0.8995 | 7.0 | 42 | 1.0631 | 0.6098 |
| 0.8995 | 8.0 | 48 | 1.0130 | 0.5854 |
| 0.7427 | 9.0 | 54 | 0.9666 | 0.6341 |
| 0.6143 | 10.0 | 60 | 0.9418 | 0.6098 |
| 0.6143 | 11.0 | 66 | 0.9096 | 0.6341 |
| 0.4971 | 12.0 | 72 | 0.8791 | 0.6341 |
| 0.4971 | 13.0 | 78 | 0.8576 | 0.6341 |
| 0.3974 | 14.0 | 84 | 0.8299 | 0.6341 |
| 0.3312 | 15.0 | 90 | 0.8125 | 0.6585 |
| 0.3312 | 16.0 | 96 | 0.7924 | 0.6585 |
| 0.2583 | 17.0 | 102 | 0.7878 | 0.6341 |
| 0.2583 | 18.0 | 108 | 0.7665 | 0.6341 |
| 0.2053 | 19.0 | 114 | 0.7402 | 0.6585 |
| 0.1711 | 20.0 | 120 | 0.7303 | 0.6585 |
| 0.1711 | 21.0 | 126 | 0.7219 | 0.6585 |
| 0.1383 | 22.0 | 132 | 0.7157 | 0.6341 |
| 0.1383 | 23.0 | 138 | 0.6921 | 0.6585 |
| 0.1073 | 24.0 | 144 | 0.6843 | 0.6585 |
| 0.0942 | 25.0 | 150 | 0.6833 | 0.6585 |
| 0.0942 | 26.0 | 156 | 0.6687 | 0.6585 |
| 0.0772 | 27.0 | 162 | 0.6726 | 0.7073 |
| 0.0772 | 28.0 | 168 | 0.6619 | 0.6585 |
| 0.0641 | 29.0 | 174 | 0.6481 | 0.6585 |
| 0.0563 | 30.0 | 180 | 0.6452 | 0.7073 |
| 0.0563 | 31.0 | 186 | 0.6508 | 0.7073 |
| 0.0487 | 32.0 | 192 | 0.6523 | 0.7073 |
| 0.0487 | 33.0 | 198 | 0.6468 | 0.7073 |
| 0.0422 | 34.0 | 204 | 0.6452 | 0.7073 |
| 0.0402 | 35.0 | 210 | 0.6443 | 0.7073 |
| 0.0402 | 36.0 | 216 | 0.6441 | 0.7073 |
| 0.0375 | 37.0 | 222 | 0.6438 | 0.7073 |
| 0.0375 | 38.0 | 228 | 0.6434 | 0.7073 |
| 0.0345 | 39.0 | 234 | 0.6428 | 0.7073 |
| 0.0342 | 40.0 | 240 | 0.6422 | 0.7073 |
| 0.0342 | 41.0 | 246 | 0.6417 | 0.7073 |
| 0.0339 | 42.0 | 252 | 0.6418 | 0.7073 |
| 0.0339 | 43.0 | 258 | 0.6418 | 0.7073 |
| 0.0327 | 44.0 | 264 | 0.6418 | 0.7073 |
| 0.0338 | 45.0 | 270 | 0.6418 | 0.7073 |
| 0.0338 | 46.0 | 276 | 0.6418 | 0.7073 |
| 0.033 | 47.0 | 282 | 0.6418 | 0.7073 |
| 0.033 | 48.0 | 288 | 0.6418 | 0.7073 |
| 0.0336 | 49.0 | 294 | 0.6418 | 0.7073 |
| 0.0342 | 50.0 | 300 | 0.6418 | 0.7073 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0
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